code: def text_list_to_colors(names):
      """
      Generates a list of colors based on a list of names (strings).
      Similar strings correspond to similar colors.
      """
      # STEP A: compute strings distance between all combnations of strings
      Dnames = np.zeros((len(names), len(names)))
      for i in range(len(names)):
          for j in range(len(names)):
              Dnames[i, j] = 1 - 2.0 * levenshtein(
                  names[i], names[j]
              ) / float(len(names[i] + names[j]))
  
      # STEP B: pca dimanesionality reduction to a single-dimension
      # (from the distance space)
      pca = sklearn.decomposition.PCA(n_components=1)
      pca.fit(Dnames)
  
      # STEP C: mapping of 1-dimensional values to colors in a jet-colormap
      textToColor = pca.transform(Dnames)
      textToColor = (
          255
          * (textToColor - textToColor.min())
          / (textToColor.max() - textToColor.min())
      )
      textmaps = generateColorMap()
      colors = [textmaps[int(c)] for c in textToColor]
      return colors
  
file_path: pyAudioAnalysis/pyAudioAnalysis/audioVisualization.py
imports: from __future__ import print_function
  import shutil, struct, simplejson
  from scipy.spatial import distance
  from pylab import *
  import ntpath
  import os
  from pyAudioAnalysis import MidTermFeatures
  from pyAudioAnalysis import audioTrainTest
  import sklearn
  import sklearn.discriminant_analysis
  import sys
  from sklearn.preprocessing import StandardScaler
  import plotly.express
label_desc: Use image node_id 3 for calling a global function with example usage: text_list_to_colors(names) and returns: colors